Sensors in electronics manufacturing: Production with all senses

Sensors are the key technology for intelligent machines and plants in networked production. As the most important data supplier, they enable the digitization of the entire value chain.

Few developments have changed the world of work as much as digitalization. The main drivers of change are ubiquitous connectivity and the use of sensors to monitor real operating conditions.

As a result, expectations for the growth industry of sensor technology are high. For example, analysts at Precedence Research expect the global sensor market to grow from $204.80 billion in 2022 to $508.64 billion in 2032, at a CAGR of 8.4 percent.

Sensors for industrial applications

The consumer and automotive sectors generate huge volumes. But in the heterogeneous industrial sensor market, much smaller lot sizes are driving up manufacturing and development costs, and thus prices. In addition, miniaturization, high-speed data transmission and ruggedness are dominating the requirements for industrial sensors. Last but not least, they are becoming increasingly intelligent. Supported by microcontrollers (MCU) and digital signal processors (DSP), measurement acquisition is followed by internal processing. As a result, in addition to the actual transducer, diagnostic functions, remote maintenance, connectivity, and computing power are housed in the same enclosure. Despite the increasing number of functions, the size of the housing should not increase.

The big challenge for the user is to select the relevant data points and the appropriate sensor technology for each application, and to extract information from the data that generates real added value.

Sensors along the production chain

  • Logistics
    Load handling, localization, environmental detection - navigating automated guided vehicles (AGVs) through factories requires a range of sensors. In the premium segment, 3D lidar with time-of-flight cameras provides distance information as well as a wealth of other data that holds the promise of additional functions beyond collision avoidance.
    Data fusion of less powerful sensors can also perform logistical tasks. For example, acoustic sensors and frequency analysis can be used to detect wear and tear on vehicles. If the maintenance status is derived from this, the result is predictive maintenance. In this case, less expensive sensors are purchased at a higher cost in the form of software or AI algorithms.
  • Quality Control
    Close-mashed, efficient quality control ensures consistently high product quality and transparency in industrial production processes. Automated image processing (machine vision) is an important component. It is based on industrial cameras with digital sensors, special optics and software. Special algorithms extract and analyze data from the images, increasingly using deep learning. Application scenarios range from the identification of specific workpieces to process or quality control.
  • Condition Monitoring, Predictive Maintenance
    Predictive maintenance programs are among the most successful scenarios in smart factories. Sensors on equipment and machines continuously record a wide range of physical parameters such as vibration, temperature, magnetic field, sound pressure, current, etc., which are then evaluated by independently learning predictive models. This allows components to be replaced proactively before failures occur.
  • Process control
    Whether it is levels, speeds, distances, or temperatures, sensors determine the status of machines and transmit it to the PLC (programmable logic controller), which uses it to automatically control processes via actuators. Further processing of the data supports troubleshooting, process analysis, condition monitoring, and energy management.
  • Human-Robot Collaboration (HRC)
    Due to the high demands on human safety, robots are usually limited in performance and power. Effective HRC requires 3D safety sensors, such as high-resolution radar or LiDAR (Light Detection and Ranging), to monitor the entire collaboration space and dynamically adjust the robot's speed and direction of movement.
  • After Sales
    Sensors on customers' machines and equipment open up new data-based business models in the form of innovative billing mechanisms such as pay-per-use, product-as-a-service (PaaS), machine-as-a-service (MaaS), or service offerings such as predictive maintenance. The latter significantly reduces downtime and maintenance costs for the plant operator. The manufacturer benefits from additional revenue and improved customer retention. What applies to the machines and systems in the manufacturer's own production thus opens up new business areas for the customer.